图像增强之线性灰度增强

图像模糊不清或曝光过度的情况下，可以通过线性灰度增强来对图像内的像素进行线性扩展，改变图片的显示效果。

转换公式： g(x,y)=((d-c)/(b-a))(f(x,y)-a)+c

其中，f(x,y)为转换前图像，其灰度范围为[a,b];g(x,y)为转换后图像，其灰度范围为[c,d]。

//线性灰度增强 g(x,y)=[(d-c)/(b-a)]*(f(x,y)-a)+c
bool GrayLinearTransform(IplImage* src, IplImage* dst, uchar dstStart, uchar dstEnd)
{
uchar* srcData = (uchar*)src->imageData;
//获取图像最大最小灰度(分情况可简化为255-0)
int imageMax,imageMin;
imageMax = imageMin = srcData[0];
for( int x = 0; x < src->height; x++)
for(int y = 0; y < src->width; y++)
{
int value = srcData[x*src->widthStep+y];
if( imageMax < value)
imageMax = value;
else
if(imageMin>value)
imageMin = value;
}
float linearRatio = float(dstEnd-dstStart)/(imageMax-imageMin);
for( int x = 0; x < src->height; x++)
for(int y = 0; y < src->width; y++)
{
int step = src->widthStep;
int a = srcData[x*step+y];
int bb = linearRatio*(a-imageMin)+ dstStart;
dst->imageData[x*step+y] = bb;
}
return true;
}

在OpenCV中，函数
cvNormalize( const CvArr* src, CvArr* dst,
double a CV_DEFAULT(1.), double b CV_DEFAULT(0.),
int norm_type CV_DEFAULT(CV_L2),
const CvArr* mask CV_DEFAULT(NULL) );

也可实现相同功能：
// HelloOpenCV.cpp : 定义控制台应用程序的入口点。
//

#include "stdafx.h"
#include <cv.h>
#include <cxcore.h>
#include <highgui.h>

// 隐藏 console 窗口
#pragma comment( linker, "/subsystem:\"windows\" /entry:\"mainCRTStartup\"" )

//线性灰度增强 g(x,y)=[(d-c)/(b-a)]*(f(x,y)-a)+c
bool GrayLinearTransform(IplImage* src, IplImage* dst, uchar dstStart, uchar dstEnd)
{
uchar* srcData = (uchar*)src->imageData;
//获取图像最大最小灰度(分情况可简化为255-0)
int imageMax,imageMin;
imageMax = imageMin = srcData[0];
for( int x = 0; x < src->height; x++)
for(int y = 0; y < src->width; y++)
{
int value = srcData[x*src->widthStep+y];
if( imageMax < value)
imageMax = value;
else
if(imageMin>value)
imageMin = value;
}
float linearRatio = float(dstEnd-dstStart)/(imageMax-imageMin);
for( int x = 0; x < src->height; x++)
for(int y = 0; y < src->width; y++)
{
int step = src->widthStep;
int a = srcData[x*step+y];
int bb = linearRatio*(a-imageMin)+ dstStart;
dst->imageData[x*step+y] = bb;
}
return true;
}

int main( int argc, char** argv )
{
cvNamedWindow( "win",1);
::cvMoveWindow("win",300,100);
IplImage* src = cvLoadImage( "C:\\Users\\Administrator\\Pictures\\lena.jpg", 1 );
//cvShowImage("win", src);
//cvWaitKey(0);
IplImage * dst_gray = cvCreateImage( cvGetSize(src), src->depth, 1);
cvSetZero(dst_gray);
cvCvtColor(src,dst_gray,CV_BGR2GRAY);//得到灰度图
cvShowImage("win", dst_gray);
cvWaitKey(0);
IplImage* transform = ::cvCreateImage(cvGetSize(dst_gray),dst_gray->depth,1);
cvSetZero(transform);
if(GrayLinearTransform(dst_gray,transform,100,200))
{
cvShowImage("win", transform);
cvWaitKey(0);
}
IplImage* transform2=::cvCreateImage(cvGetSize(dst_gray),dst_gray->depth,1);
cvSetZero(transform2);
cvNormalize(dst_gray,transform2,255,0,CV_MINMAX,NULL);
cvShowImage("win", transform2);
cvWaitKey(0);
::cvReleaseImage(&src);
::cvReleaseImage(&dst_gray);
::cvReleaseImage(&transform);
::cvReleaseImage(&transform2);
::cvDestroyWindow("win");
return 0;
}

转换前，灰度图像：



将灰度区间缩小为[100,200]得到图片：



再将灰度图片由[100,200]放大至[0,255]:



 经过缩放后的图片有一定的“锐化”效果